Summary Several aspects common to a Western lifestyle, including obesity and decreased physical activity, are known risks for gastrointestinal cancers1. There is substantial evidence suggesting that diet profoundly affects the composition of the intestinal microbiota2. Moreover, there is now unequivocal evidence linking dysbiosis to cancer development3. Yet the mechanisms through which high-fat diet (HFD)-mediated changes in the microbial community impact the severity of tumorigenesis in the gut remain to be determined. Here we demonstrate that HFD promotes tumor progression in the small intestine of genetically susceptible K-rasG12Dint mice independently of obesity. HFD consumption in conjunction with K-Ras mutation mediates a shift in the composition of gut microbiota, which is associated with a decrease in Paneth cell antimicrobial host defense that compromises dendritic cell (DC) recruitment and MHC-II presentation in the gut-associated lymphoid tissues (GALTs). DC recruitment in GALTs can be normalized, and tumor progression attenuated, when K-rasG12Dint mice are supplemented with butyrate. Importantly, Myd88-deficiency blocks tumor progression. Transfer of fecal samples from diseased donors into healthy adult K-rasG12Dint mice is sufficient to transmit disease in the absence of HFD. Furthermore, treatment with antibiotics completely blocks HFD-induced tumor progression suggesting a pivotal role for distinct microbial shifts in aggravating disease. Collectively, these data underscore the importance of the reciprocal interaction between host and environmental factors in selecting microbiota that favor carcinogenesis, and suggest tumorigenesis may be transmissible among genetically predisposed individuals.

Developmental programs that generate the astonishing neuronal diversity of the nervous system are not completely understood and thus present a significant challenge for clinical applications of guided cell differentiation strategies. Using direct neuronal programming of embryonic stem cells, we found that two main vertebrate proneural factors, Ascl1 and Neurog2, induce different neuronal fates by binding to largely different sets of genomic sites. Their divergent binding patterns are not determined by the previous chromatin state but are distinguished by enrichment of specific E-box sequences which reflect the binding preferences of the DNA-binding domains. The divergent Ascl1 and Neurog2 binding patterns result in distinct chromatin accessibility and enhancer activity profiles that differentially shape the binding of downstream transcription factors during neuronal differentiation. This study provides a mechanistic understanding of how transcription factors constrain terminal cell fates, and it delineates the importance of choosing the right proneural factor in neuronal reprogramming strategies.

SUMMARY Direct cell programming via overexpression of transcription factors (TFs) aims to control cell fate with the degree of precision needed for clinical applications. However, the regulatory steps involved in successful terminal cell fate programming remain obscure. We have investigated the underlying mechanisms by looking at gene expression, chromatin states, and transcription factor binding during the uniquely efficient Ngn2, Isl1, and Lhx3 motor neuron programming pathway. Our analysis reveals a highly dynamic process in which Ngn2 and the Isl1/Lhx3 pair initially engage distinct regulatory regions. Subsequently, Isl1/Lhx3 binding shifts from one set of targets to another, controlling regulatory region activity and gene expression as cell differentiation progresses. Binding of Isl1/Lhx3 to later motor neuron enhancers depends on the TFs Ebf and Onecut, which are induced by Ngn2 during the programming process. Thus, motor neuron programming is the product of two initially independent transcriptional modules that converge with a feedforward transcriptional logic.

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Cellular reprogramming experiments from somatic cell types have demonstrated the plasticity of terminally differentiated cell states. Recent efforts in understanding the mechanisms of cellular reprogramming have begun to elucidate the differentiation trajectories along the reprogramming processes. In this review, we focus mainly on direct reprogramming strategies by transcription factors and highlight the variables that contribute to cell fate conversion outcomes. We review key studies that shed light on the cellular and molecular mechanisms by investigating differentiation trajectories and alternative cell states as well as transcription factor regulatory activities during cell fate reprogramming. Finally, we highlight a few concepts that we believe require attention, particularly when measuring the success of cell reprogramming experiments.

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Background Transcription factor (TF) binding specificity is determined via a complex interplay between the transcription factor’s DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with transcription factor binding in a given cell type have been well characterized. For instance, the binding sites for a majority of transcription factors display concurrent chromatin accessibility. However, concurrent chromatin features reflect the binding activities of the transcription factor itself and thus provide limited insight into how genome-wide TF-DNA binding patterns became established in the first place. To understand the determinants of transcription factor binding specificity, we therefore need to examine how newly activated transcription factors interact with sequence and preexisting chromatin landscapes. Results Here, we investigate the sequence and preexisting chromatin predictors of TF-DNA binding by examining the genome-wide occupancy of transcription factors that have been induced in well-characterized chromatin environments. We develop Bichrom, a bimodal neural network that jointly models sequence and preexisting chromatin data to interpret the genome-wide binding patterns of induced transcription factors. We find that the preexisting chromatin landscape is a differential global predictor of TF-DNA binding; incorporating preexisting chromatin features improves our ability to explain the binding specificity of some transcription factors substantially, but not others. Furthermore, by analyzing site-level predictors, we show that transcription factor binding in previously inaccessible chromatin tends to correspond to the presence of more favorable cognate DNA sequences. Conclusions Bichrom thus provides a framework for modeling, interpreting, and visualizing the joint sequence and chromatin landscapes that determine TF-DNA binding dynamics.

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